Amortised and provably-robust simulation-based inference

TL;DR

This paper introduces a robust, amortised simulation-based inference method that handles outliers effectively and avoids MCMC sampling, significantly reducing computational costs.

Contribution

It presents a novel approach combining generalized Bayesian inference with neural weighted score-matching, achieving robustness and efficiency in simulation-based inference.

Findings

Method is provably robust to outliers.

Inference is performed without MCMC sampling.

Computational complexity is significantly reduced.

Abstract

Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other extreme values in data which occur due to faulty measurement instruments or human error. In this paper, we introduce a novel approach to simulation-based inference grounded in generalised Bayesian inference and a neural approximation of a weighted score-matching loss. This leads to a method that is both amortised and provably robust to outliers, a combination not achieved by existing approaches. Furthermore, through a carefully chosen conditional density model, we demonstrate that inference can be further simplified and performed without the need for Markov chain Monte Carlo sampling, thereby offering significant computational advantages, with complexity that is only a small fraction of that of current state-of-the-art approaches.